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File Version Author Date Message
Rmd 0c92da2 Andreas Chiocchetti 2024-01-03 setup as workflowr

Clustering Analysis

Clustering after batch correction

Centering and scaling data matrix
PC_ 1 
Positive:  STMN2, NSG2, NEUROD6, INA, NEUROD2, BHLHE22, CXADR, SLA, GAP43, NELL2 
       TTC9B, GRIA2, MYT1L, LRRC7, MLLT3, LBH, UCHL1, DLX6-AS1, STMN4, OCIAD2 
       CNR1, SYT4, ENC1, MEF2C, SNAP25, RUNX1T1, ERBB4, SNCB, ZBTB18, GRIA1 
Negative:  SLC1A3, VIM, HMGB2, ZFP36L1, NUSAP1, TOP2A, DBI, PTN, B2M, MKI67 
       PTTG1, CLU, CD99, PTPRZ1, CDK1, SPARC, PON2, HOPX, UBE2C, METRN 
       TTYH1, TPX2, PBK, CENPF, ANXA5, MT2A, BCAN, SOX9, PEA15, HSPB1 
PC_ 2 
Positive:  MKI67, UBE2C, TOP2A, NUSAP1, TPX2, CENPF, KIF2C, DLGAP5, ASPM, BIRC5 
       KNL1, PIMREG, CDC20, CDCA8, NUF2, PBK, CDK1, SGO1, PTTG1, HMGB2 
       KIF11, PLK1, CCNA2, CKAP2L, KIFC1, GTSE1, CCNB1, NDC80, CENPE, MAD2L1 
Negative:  CLU, ATP1B2, PTN, AQP4, HOPX, PON2, TFPI, ANOS1, ATP1A2, TTYH1 
       SPARC, BCAN, SLC1A3, APOE, PSAT1, VIM, PEA15, FAM107A, PTPRZ1, HES1 
       TIMP3, IL33, LRRC3B, CSPG5, S1PR1, SLCO1C1, SCD, VCAM1, TNC, IQGAP2 
PC_ 3 
Positive:  NEUROD6, NELL2, NEUROD2, MEF2C, BHLHE22, GAP43, ARPP21, SATB2, SERPINI1, SYT4 
       ZBTB18, SLA, FAM49A, GPR22, NEFM, CAMK2B, GPR85, SNCB, CSRP2, NSG2 
       CXADR, LINGO1, SATB2-AS1, PLXNA4, DAB1, OCIAD2, GPRIN3, NRN1, PCLO, FAM162A 
Negative:  DLX6-AS1, PLS3, SCGN, DLX2, DLX5, DLX1, SOX2-OT, GAD2, CALB2, SP9 
       PDZRN3, ERBB4, RND3, ID4, SMOC1, C1orf61, NNAT, GAD1, WLS, SOX9 
       NRIP3, TOX3, ST18, HMGN2, AMBN, NRXN3, CDCA7, DBI, BCAN, PCDH9 
PC_ 4 
Positive:  ADM, VEGFA, DDIT4, BNIP3, IGFBP2, P4HA1, PLOD2, EGLN3, SLC16A3, SLC2A1 
       FAM162A, ENO1, STC2, AKAP12, PGK1, IGFBP5, GAPDH, PDK1, SLC16A1, TPI1 
       AK4, CEBPB, PKM, MIR210HG, HERPUD1, SHMT2, EMX2, HK2, BHLHE40, CDKN1A 
Negative:  NTRK2, AQP4, SPARCL1, APOE, GJA1, CST3, SPON1, MEF2C, PMP2, ANOS1 
       TFPI, S100B, BCAN, CHL1, STMN2, DCLK1, CSPG5, NKAIN4, BBOX1, VCAM1 
       LINC01896, AGT, CALM1, SATB2, ANGPT1, RANBP3L, SERPINI1, WLS, NELL2, GFAP 
PC_ 5 
Positive:  ENC1, TMEM158, BHLHE22, NEUROD2, EZR, SLA, CSRP2, MLLT3, CNR1, PHLDA1 
       NEUROD6, CNTNAP2, LHX2, ADRA2A, NKAIN3, ZBTB18, HES6, EOMES, EPHA3, CLMP 
       NHLH1, FABP7, RASGRP1, NEUROG2, SFRP1, CHRDL1, HS3ST1, PENK, GAP43, GNG5 
Negative:  DLX6-AS1, PLS3, SCGN, DLX2, SOX2-OT, DLX1, DLX5, PLOD2, STC2, PDZRN3 
       GPRIN3, GPR22, CALB2, ADM, DDIT4, CALY, BNIP3, SERPINI1, GAD2, ERBB4 
       VEGFA, H1F0, FAM162A, SP9, PCDH9, IGFBP5, CNTN1, CELF4, PDK1, P4HA1 
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9383
Number of communities: 7
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9007
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8746
Number of communities: 14
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8516
Number of communities: 14
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8312
Number of communities: 16
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8164
Number of communities: 19
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8028
Number of communities: 20
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7900
Number of communities: 21
Elapsed time: 0 seconds
UMAP Clustering after batch correction at different resolutions

UMAP Clustering after batch correction at different resolutions

Saving 8 x 8 in image

Check stability of clusters

Saving 16 x 8 in image
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9107
Number of edges: 410519

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8746
Number of communities: 14
Elapsed time: 0 seconds

optimize UMAP

<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
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Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).
Removed 1 rows containing missing values (`geom_text()`).

Saving 12 x 12 in image
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
18:21:14 UMAP embedding parameters a = 0.1496 b = 0.8684
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by 'spam'
18:21:14 Read 9107 rows and found 40 numeric columns
18:21:14 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by 'spam'
18:21:14 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:21:15 Writing NN index file to temp file /tmp/Rtmp0nn1gr/file1cf0efde06b
18:21:15 Searching Annoy index using 1 thread, search_k = 3000
18:21:17 Annoy recall = 100%
18:21:18 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
18:21:19 Initializing from normalized Laplacian + noise (using RSpectra)
18:21:20 Commencing optimization for 500 epochs, with 415428 positive edges
18:21:30 Optimization finished

final UMAP clustering

Saving 7 x 5 in image

Saving 8 x 4 in image

Calculate cell cycle scoring

Warning: The following features are not present in the object: FEN1, MLF1IP,
RAD51, not searching for symbol synonyms
Warning: The following features are not present in the object: FAM64A, HN1, not
searching for symbol synonyms

Saving 12 x 6 in image

Identification of clusters

          0mM       5mM
0mM 1.0000000 0.6908695
5mM 0.6908695 1.0000000
Using type as id variables

Saving 8 x 8 in image
Feature plots UMAP

Feature plots UMAP

Saving 8 x 8 in image
Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
Feature plots PCA

Feature plots PCA

Saving 8 x 8 in image

Markers identification and visualization

Calculating cluster 0
For a (much!) faster implementation of the Wilcoxon Rank Sum Test,
(default method for FindMarkers) please install the presto package
--------------------------------------------
install.packages('devtools')
devtools::install_github('immunogenomics/presto')
--------------------------------------------
After installation of presto, Seurat will automatically use the more 
efficient implementation (no further action necessary).
This message will be shown once per session
Calculating cluster 1
Calculating cluster 2
Calculating cluster 3
Calculating cluster 4
Calculating cluster 5
Calculating cluster 6
Calculating cluster 7
Calculating cluster 8
Calculating cluster 9
Calculating cluster 10
Calculating cluster 11
Calculating cluster 12
Calculating cluster 13
Warning in DoHeatmap(seurat_integrated, features = top10$gene, slot =
"scale.data"): The following features were omitted as they were not found in
the scale.data slot for the RNA assay: CYP4F26P, PPARG, IGLV1-51, TRHDE-AS1,
AL133375.1, DCHS2, AC026471.3, CNOT6LP1, AC103810.2, ARMC3, LINC01497,
AC084125.2, AC120036.1, POU4F1, NAMPTP1, AC010320.1, LHX5-AS1, AC091182.1,
NKX2-5, HOXD9, MYOD1, LHX5, ULBP1, AL157400.2, UPK1A-AS1, AC099850.3, FAM72C,
FBLN2, HSPB3, MYO3B, IMPG2, VIPR2, LINC00689, TESK2, AP001972.3, DENND1C,
CRYBG2, MCHR1, RELN, EPS8L2, ASCL2, CABP7, FXYD3, PI16, KCNJ16, GJB2, FIBIN,
MME, OXTR, DMRTA1, AC087632.1, KANK2, CDC45, AC099754.1, AC092112.1, IL12A,
CASP1, FMO1, TMEM244, GPR61, AL139275.1, AC010931.2, AKAIN1, CEMIP, LRTM2,
UNC5A, LAMB3, PTGFR, SVEP1, MYOT, CACNA2D3, PTPRR, RGN, THRB

Saving 8 x 15 in image

Visualizing the expression of marker genes with respect to different cell types

Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Using Group.1 as id variables

Saving 6 x 9 in image

GO terms of clusters

Detected custom background input, domain scope is set to 'custom'
[1] "result" "meta"  

Mapping of clusters to reference data sets

Knoblich Clusters

Warning in data_preprocess(expr, anno_processed): The following specified
marker genes are not found from the expression data: CRACD, H3-3B, SOX2, TLE5,
MARCHF6, RPS13, ARMH4, RPL38, BMERB1, RPS6KA2, TENT2, SEPTIN3, DOP1A, RPS27L,
RPS19, RPS6, RPS11, RPL15, RPL7, RPS14, RPS8, RPS7, RPL13A, RPL8, RPS2, RPL6,
RPL35A, CEMIP2, RPL27A, RPLP0, RPL5, RPS25, RPS29, RPS27A, RPL41, HNRNPA1,
RPL10A, RPL4, RPL19, RPL3, RPS3, RPL7A, SEPTIN11, RPL28, RPLP1, RPS9, RPL23,
RPL13, RPS20, RPL10, RPL23A, RPL11, RPS15, RPL21, RPS27, RPS16, RPS4X, RPL14,
RPL35, H2AZ2, RPL29, RPSA, RPL12, RPL27, RPS12, DARS1, RPL18, RPL36, RPS3A,
SSTR2, DOP1B, ITPRID1, ASNS, AC025159.1, MARCHF1, RPS6KL1, TAFA1, BAIAP2-DT,
SEPTIN6, PWAR6, AC124312.1, TAFA5, TPTEP2-CSNK1E, SARS1, POU3F3, PLAAT3,
ITPRID2, LRATD2, LINC02588, SEPTIN7, MT-CO1, MT-ND3, GASK1B, MT-CO2, MT-CYB,
RPL22L1, MT-CO3, MICOS13, PRXL2A, RPS18, SEPTIN2, MT-ATP6, RPS23, RPS5, RPS24,
MT1X, NARS1, RPL32, MT-ND4, RPS28, RPS15A, MT-ND1, RPL37, RPL30, RPLP2, RPL37A,
RPL34, SNHG32, RPL31, RPL22, RPS17, LARS1, RPL24, MT-ND6, RPL39,
AFTER-FLOX.FLAG.NLS.CAS9.DTOM.WPRE.HAL.SV40,
ICROP-CHONG.GUIDES.AND.BC.MASKED.FIX, LINC02197, MARCHF4, MRTFA, AC009403.1,
ILF3-DT, TAFA2, SHLD2, RAB30-DT, MT-ND5, SLC49A4, CDKL3, GARS1, MT-ND2, H4C3,
H1-5, H1-2, H1-3, H3C2, H2AC20, H1-4, H2AC11, H2AC12, H3C8, H2AC14, H2BC9,
H4C4, H3C7, H2AC8, H4C8, H2BC4, H3C12, H2AC13, H2BC13, H2AC21, H2AC17, H4C5,
RRM2.1, H2BC15, H2AX, H3C10, AC091057.6, H2BC18, H2AC4, H2AC15, H3C11, H2BC17,
H2AZ1, H2BC7, H3C6, H2AC6, MACROH2A1, SEPTIN10, EFCAB11, H1-10, UMAD1,
AC006296.1, H2BC5, H2AW, H2BC8, H2BC6, MRPS6, H1-0, CRPPA, IDO1, AL353747.4,
LINC00513.1, AL035078.4, SEPTIN9, ARL6IP1, CALM2, TENT4A, RPL39L, CU633967.1,
AP002495.2, PLAAT1, GMDS-DT, SLC10A1, IARS1, CAPN10-DT, AL355075.4, ZNF229,
LNCTAM34A, CERT1, AARS1, RPS26, RPS10-NUDT3, ELFN2, AL662796.1, LINC02160,
CCN3, MARCHF3, AC092040.1, NUP50-DT, DIPK1A, RPS6KA6, MT-ND4L, RARS1, NECAP1,
DHRS4L2, H2AJ, RELCH, RPS6KA5, SNHG30, MT-ATP8, MRTFB, MTRNR2L8, MTRNR2L12,
DPH6-DT, CCN4, TUT4, COL3A1, POSTN, AL513283.1, SRPX2, FGF10-AS1, BGN,
AC010737.1, HEYL, ALDH1A3, CYTL1, EDN3, LINC01116, ABCA9, CH25H, CXCL6, LAMC3,
PIK3R6, SIM1, MYHAS, TBX2, IL1R1, CXCL1, CCL11, LINC02172, APELA, CARMN,
VSIG10L2, DPT, AC112721.2, RFX8, AC062004.1, TNFRSF4, TNFRSF18, DIRC1, MYL9,
PRF1, HTRA3, GGT5, CAVIN3, SH3TC1, RBMS3-AS2, LINC01568, HOXD3, XAF1, TRIM38,
WNT2, PALM2AKAP2, HOXB3, FLI1, CSPG4, AC005291.1, FMOD, MRGPRF, LTBR, TMEM204,
HOXD10, FZD10, GPR174, OLFML1, LINC01117, LINC01679, TNNT3, AC026904.4, TXNDC5,
AC092114.1, AC116345.1, MEOX1, LINC02507, VDR, POGLUT3, LY96, ABO, AC090617.5,
DENND2B, TENT5A, ADAMTS9-AS1, NIBAN1, OGN, AC098850.3, AC090617.4, FGL1, CD40,
WARS1, CCDC200, FP671120.1, TASOR2, FP236383.1, TARS1, AC096711.2, RPS21, FBH1,
ARL6IP4, RPL36AL.

Saving 7 x 5 in image

             Cluster
KnoblichType   0  1  2  3  4  5  6  7  8  9 10 11 12 13
  knoblich_01 71  3  4  5  0  6  0  1 28  0 12  5 43 28
  knoblich_02  0  0  0  3  1  0 18  0  9  1  2  1  1  0
  knoblich_03  2  5 28 11  0  2  2  4  1  5 19  2  5  0
  knoblich_04  0  0  0 12  1  0 32  1  0  0  5  0  0  0
  knoblich_05  0  7  6  2  0  1  0 20  0  0  6 59  5  1
  knoblich_06  0  0  0  0  4  0  0  0  0 11  0  0  0  0
  knoblich_07 20 41 43 23 28 50 13 42 44 33 33 20 36 19
  knoblich_08  0 20  0 30 12 25  7 10  7  3 10  1  0  0
  knoblich_09  3  0  0  0  6  2  1  1  9 31  0  0  0  0
  knoblich_10  1  0  0  9 44  1 22  0  0  7  1  1  0  0
  knoblich_12  0  0  2  0  0  0  1  0  0  0  3  0  0  0
  knoblich_13  2  4  9  0  0  1  1  5  0  0  0  5  7  5
  knoblich_14  1 17  1  0  0  3  1 16  0  1  0  6  3  1
  knoblich_15  0  0  0  3  0  0  2  0  0  0  4  0  0  0
  knoblich_16  0  3  7  2  4  9  0  0  2  8  5  0  0  0
Warning in chisq.test(crosstab): Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  crosstab
X-squared = 2429.9, df = 182, p-value < 2.2e-16

Knoblich Map to annotation

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Saving 10 x 5 in image
    
     Astrocytes ccRG ccvRG CGE_IN CGE_LGE_IN  INP   IP  L23   L4  L56 L6_CThPN
  0           1    0     1   1852          7   20    2    0    0    3        0
  1          16    0     0     15          0    3  119   63   53 1428       13
  2          11    0     0      8          0    0    3  585   11   64      188
  3         158   34     0      1          0    1    7    0    0    1        0
  4          10  219   287      8          0   81    3    0    0    0        0
  5          11   22     7      5          1    4  387    0    0  143        0
  6         431    0    15      1          0   34    2    0    0    0        0
  7           5    0     0     12          0    0   43   58    8  364       39
  8           3    0     6    208         10  160    0    0    0    0        0
  9           0  135   214      0          0    1    1    0    0    0        0
  10         75   11     0      3          0    3   17    0    0    0        0
  11         10    0     1     30          1    0   28   20    1   53       81
  12          4    0     1    202          1    9    0    1    0    1        0
  13          0    0     0     54          0    0    0    0    0    0        0
    
     LGE_IN mesenchyme  oRG   RG  vRG
  0     121          0    1    0    0
  1       1          0    1    0    0
  2       0          0    0    0    0
  3       0          0  432   12    1
  4       1          0    1    0    7
  5       0          0    7    0    0
  6       1          0    0    0   67
  7       0          0    0    0    0
  8      12          0    0    0    0
  9       0          0    0    0    0
  10      0          1  217    4    1
  11      1          0    0    0    0
  12      5          0    0    0    0
  13      0          0    0    0    0

Saving 12 x 12 in image

Kanton et al Table 4 Celltypes

Warning in data_preprocess(expr, anno_processed): The following specified
marker genes are not found from the expression data: SSTR2, HMP19, VAMP2, HN1,
NEUROD1, TMEM35, PAK7, MLLT4, SEP 03, ATP1A3, COMMD3, RPS6KL1, AC004158.3,
RP11-490M8.1, TMEM57, NDUFC2, NECAP1, RP11-395G23.3, RPL36A, ASNS, ALDOC,
RP11-148B6.1, POU5F1, L1TD1, TDGF1, LIN28A, LINC00678, RPS2, RPL12, MT-CO2,
FOXD3-AS1, LECT1, KIAA0101, MYL9, RP11-1144P22.1, MT-ND4, RPS6, MT-CO3, RPL8,
D21S2088E, MT-CO1, RPL39L, MT-ND6, RPLP1, RPL22L1, RPS18, MT1X, OAZ1, MT-CYB,
RPS29, TSTD1, RPL3, ZFP42, EEF1E1, RPS12, RPL15, FKBP1A, RPS3, APELA, ARL2,
PLPP2, RP11-568A7.4, GCSH, PRR13, RPL7, EMG1, RPS27A, RPS9, RPSA, FAM60A,
ATP5I, MT-ATP6, RPL18A, STRA13, CLDN7, SEPW1, TAF9, RP11-12G12.7, NANOG,
RPL13A, RP11-469A15.2, RPLP0, PWAR6, RPL23A, RPS5, RPS23, RPL19, C11ORF73,
FAM195A, RPL10A, RPS19BP1, MT-ND1, RPS7, PMF1, CNPY2, NMRK2, SPTSSA, SHFM1,
ZSCAN10, RPS3A, FOXH1, FXN, EXOSC3, TMEM14B, MRPL12, RPL11, C11ORF31, IRX2,
RPS28, GNB2L1, RPS16, RPS14, RPL28, RPS19, RPL13, RPL27A, RPS27, RPL35,
RPL36AL, B3GNT7, RP11-132A1.3, DANT1, PRKCDBP, TRIML2, RPL4, RPS4X, PARL,
ARL6IP4, RPS26, RPL7A, RP11-89K21.1, RPS20, RPL14, SEPP1, RPL10, RPS8, HILPDA,
RPL18, RPL29, TMEM261, RPL5, RPL9, FEN1, C14ORF166, RPL24, HN1L, RPS17, OVOS2,
RPL21, RP11-11N9.4, RPL23, MRPS6, ATP5G3, NME1, ADSL, RP11-20D14.6, FAM64A,
CTSD, LIMD2, ATP5G1, ATPIF1, ATP5H, UFD1L, ATP5B, SDHD, C19ORF60, APOA1BP,
NAA10, CLN6, MT-ND2, POU5F1B, ATP5A1, HRSP12, LINC00545, NGFRAP1, ATP5F1,
C14ORF1, ATP5D, CALM2, AC004540.4, C10ORF35, SEP 06, WHSC1L1, ATP5E, PRRT2,
LINC01420, NDUFB8, FAM127A, SMARCC2, FAM65B, C11ORF96, RPL38, C19ORF43, SEP 07,
USMG5, FAM215B, APOPT1, WBP5, MT-ND4L, PNMAL1, PRR4, ZNF503, GUCY1A3, LHFP,
MT-ND3, RP11-382A20.3, MT-ND5, HSD11B1L, LINC00969, TCTEX1D2, SOX2, RPS27L,
RPS24, C8ORF4, RPL39, RPL41, RPS15, RPL37, RPS15A, RPS11, RPL36, RPL30, RPL37A,
RPL35A, RPS21, RPS10, RPL17, SEP 11, FAM92A1, GLTSCR2, RP3-395M20.12, RPL6,
ATP5G2, SEP 02, LINC00998, RP11-620J15.3, HIGD1A, BORCS7, KDELR2, SIX6,
HNRNPA1, COL13A1, RP11-96L14.7, RPL34, RPL26, FAM175A, PVRL2, SGOL2, SGOL1,
CASC5, ARHGAP11B, KIAA1524, GSX2, SPAG5, COX8A, POU3F3, TP53I13, CCDC109B,
WHSC1, RHNO1, ARL6IP1, PTGDS, RP11-849I19.1, UG0898H09, RP11-436D23.1,
CTD-2336O2.1, SPG20, RP3-525N10.2, KIAA1715, SELK, GBAS, LEPROT, EIF4A1,
RP11-356J5.12, ADRBK2, GUCY1B3, FAM134B, KIAA2022, RPRM, C6ORF1, PCDHA12,
LINC00657, RP1-39G22.7, FAM134A, ABHD14A, EBLN3, SATB1, MIF, RPL22,
RP11-798M19.6, RPS13, BEND5, CRYZL1, FAM63B, RPS25, RPL32, RPL31, RPLP2, RPL27,
RAD51, C9ORF142, CTB-193M12.5, METTL10, CTB-50L17.10, SEP 10, VKORC1, ATP5J2,
NEDD8, C14ORF2, TCEB2, TCEB1, PSMA2, TMEM256, TMEM141, RBM7, TIMM10B, COL3A1,
BGN, POSTN, OGN, CDC42EP5, INSC, IGF2, TFAP2B, CYTL1, FXYD1, IFITM1, SELM,
PDLIM2, SEP 09, ARPC1A, PDE6H, PGM5P3-AS1, LINC00305, TRAC, C1ORF168, SLC4A5,
PTRF, MICA, GRAMD3, AC007325.4, SLC5A3, ORAI3, UQCR11, TMEM205, SYS1, UNC119,
ATP5L, MMP24-AS1, BLOC1S1, MINOS1, C7ORF73, HIGD2A.

Saving 18 x 5 in image

Saving 18 x 5 in image

                                   Cluster
KantonT4Type                         0  1  2  3  4  5  6  7  8  9 10 11 12 13
  choroid plexus                     0  0  1  3  0  0  2  0  0  0  7  0  0  0
  cortical neurons 1                 0  0  9  0  0  0  1  1  0  0  2  6  0  0
  cortical neurons 2                 2  5 33  1  0  3  0  4  1  1  7  5  6  5
  G2M/S dorsal progenitors 1         0  0  0  1  0  0  1  0  0  1  0  0  0  0
  G2M/S dorsal progenitors 2         0  0  0  2  9  0  1  0  0  7  4  1  0  0
  G2M/S NPCs                         0  0  0  0 23  0  1  0  0 19  0  0  0  0
  G2M/S ventral progenitors and IPs  0  1  0  0 25  4  2  1  1 39  0  0  0  0
  gliogenic/outer radial glia        1  0  1 29  1  1 44  2  1  4 33  1  4  1
  IPs and early cortical neurons     0 28  3  6 13 51  6 23 28  4  2  4  1  0
  mesenchymal-like cells             0  0  0  3  1  0  5  0  0  0  4  0  0  0
  midbrain/hindbrain                 6 14 11  5  0  5  1 11  7  0  8  4  1  6
  neuroectodermal-like cells         0  0  0  0  0  0  1  1  0  0  0  0  0  0
  NSC/radial glia                    0  0  0  0  0  0  2  0  0  0  0  0  0  0
  radial glia 1                      0  0  0 20  0  0  6  0  0  0  0  0  0  0
  radial glia 2                      0  0  0  0  0  0  1  0  0  0  2  0  0  0
  stem cells 1                       0  0  0  1  0  0  0  0  0  0  4  0  0  0
  stem cells 2                       0  0  0  4  5  0  6  0  0  2  1  1  0  0
  stem cells 3                       0  0  0  5  6  1  4  1  1  0  2  0  0  0
  ventral progenitors and neurons 1 41 35 34  7  6  7  8 37 34  8  6 21 40 29
  ventral progenitors and neurons 2 47  7  1  3  3  3  6 17 18  1 13 56 45  8
  ventral progenitors and neurons 3  3  9  7  9  7 20  0  2  8 12  5  1  3  5
  ventral progenitors and neurons 4  0  1  0  1  1  5  2  0  1  2  0  0  0  0
Warning in chisq.test(crosstab): Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  crosstab
X-squared = 2415, df = 273, p-value < 2.2e-16

Kanton et al Table 14 Celltypes

Warning in data_preprocess(expr, anno_processed): The following specified
marker genes are not found from the expression data: KCNIP4-IT1, MEG3, CALM2,
SATB1, MAG, PTGDS, CLDN11, MIR219A2, KLK6, OPALIN, SEP 04, RNASE1, SEP 07,
ATP5E, RPS9, RPS26, MT-CO2, RPL27A, RPL18, RPS15, RPL10, RPL19, RPL13, NDUFA13,
RPL13A, RPL21, ATP5I, RPS27A, RPS28, RPL37, RPS18, RPL35, RPL24, RPL32, RPLP1,
C14ORF2, RPL28, RPS27, RPL35A, RPL23A, RPS2, RPS19, ATP1A3, PEG3, HGNC:24955,
ALDOC, PWAR6, ARL6IP1, MT-CO3, MT1X, CST1, FXYD1, RPS25, RPL41, RPS24, RPL31,
RPL26, RPL36A, RPL11, RPS20, RPS12, RPS23, SLC38A11, RPS6, RPL37A, RPL34,
RPS14, RPL36AL, RPS13, RPS21, RPL7, RPS29, COX7A1, RPL23, RPL38, RPS3A, RPL30,
RPL39, RPL36, RPL12, RPL22, RPS10, RPS8, RPL27, RPLP2, RPL14, RPS7, ATP5EP2,
RPS16, RPS4X, RPL9, RPS3, UQCR11, RPS17, RPL5, MINOS1, RPSA, TESPA1, USMG5,
RPS15A, RPL6, RPS11, RPL18A, ATP5J2, PPP3R1, RPL8, ATPIF1, RPL3, RPL15, ATP5G2,
ATP5L, RPL4, RPL10A, VAMP2, COX8A, RPS5, SELENOH, NEDD8, MT-ND1, TGM2, ADIRF,
MT-CYB, MT-ND2, OAZ1.

            Cluster
Kanton14Type  0  1  2  3  4  5  6  7  8  9 10 11 12 13
     Ast      3  3 19 35  4  3 35  3  2  5 51  3  7  1
     End/Per  3 37  4 25 39 51 12 29 37 26 20 38 15 13
     Ex      31 34 22  5  7  8  2 39  6  4  5 42 27 24
     In      40  0  0  0  1  0  1  0 10  0  0  4 13  6
     Mic     11  3  4  6  2  5  4  4  4  2  1  1  6  1
     Oli      3 12 31 15 37 23  6  6  9 60 21  6 18  2
     OPC      9 11 20 14 10 10 40 19 32  3  2  6 14  7
Warning in chisq.test(crosstab): Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  crosstab
X-squared = 1093.7, df = 78, p-value < 2.2e-16
            used  (Mb) gc trigger   (Mb)   max used    (Mb)
Ncells  10783053 575.9   31776362 1697.1  189401815 10115.2
Vcells 119431155 911.2  645986751 4928.5 1009345594  7700.7

HumanDevData Map to annotation

.
HsapDv:0000099 HsapDv:0000100 HsapDv:0000101 HsapDv:0000102 HsapDv:0000103 
         36490         210331          16843          83258          34820 
HsapDv:0000104 HsapDv:0000105 HsapDv:0000106 HsapDv:0000107 HsapDv:0000108 
         26622          30871          25957          11630          23178 
.
                b'Brain'       b'Caudate-Putamen'            b'Cerebellum' 
                   25722                     3965                    42608 
               b'Cortex'          b'Cortical hem'          b'Diencephalon' 
                   44764                     2107                    10338 
      b'Dorsal midbrain'     b'Enthorinal cortex'      b'Forebrain cortex' 
                   13075                     3230                     4769 
            b'Forebrain' b'Frontotemporal cortex'                  b'Head' 
                   58623                     6732                     2034 
            b'Hindbrain'           b'Hippocampus'          b'Hypothalamus' 
                   14771                    10478                    14098 
         b'Lower cortex'               b'Medulla'         b'Mesencephalon' 
                    5446                    38461                    48963 
             b'Midbrain'      b'Occipital cortex'                  b'Pons' 
                    9984                     4063                    18849 
           b'Pons/Cereb'          b'Pons/Medulla'              b'Striatum' 
                   10061                     4109                    28578 
            b'Subcortex' b'Subcortical forebrain'      b'Tel/diencephalon' 
                    8736                    16143                     5033 
        b'Telencephalon'              b'Thalamus'          b'Upper cortex' 
                    2161                    33097                     4043 
     b'Ventral midbrain' 
                    4959 
.
              b'Brain'     b'Caudate+Putamen'          b'Cerebellum' 
                 25722                   3965                  42608 
  b'Cortex entorhinal'      b'Cortex frontal' b'Cortex hemisphere A' 
                  3230                   6732                   2128 
b'Cortex hemisphere B'    b'Cortex occipital'     b'Cortex parietal' 
                  3921                   4063                   4043 
    b'Cortex temporal'              b'Cortex'        b'Cortical hem' 
                  5446                  43484                   2107 
       b'Diencephalon'           b'Forebrain'                b'Head' 
                 10338                  63656                   2034 
          b'Hindbrain'         b'Hippocampus'        b'Hypothalamus' 
                 18880                   4691                  14098 
            b'Medulla'     b'Midbrain dorsal'    b'Midbrain ventral' 
                 38461                  13075                   4959 
           b'Midbrain'                b'Pons'            b'Striatum' 
                 58947                  28910                  28578 
          b'Subcortex'       b'Telencephalon'            b'Thalamus' 
                 30666                   2161                  33097 
.
           b'Brain'       b'Cerebellum'           b'Cortex'     b'Diencephalon' 
              25722               42608               75154               10338 
       b'Forebrain'             b'Head'        b'Hindbrain'      b'Hippocampus' 
              63656                2034               18880                4691 
    b'Hypothalamus'          b'Medulla'  b'Midbrain dorsal' b'Midbrain ventral' 
              14098               38461               13075                4959 
        b'Midbrain'             b'Pons'         b'Striatum'        b'Subcortex' 
              58947               28910               32543               30666 
   b'Telencephalon'         b'Thalamus' 
               2161               33097 
.
                b'Brain'       b'Caudate-Putamen'            b'Cerebellum' 
                   25722                     3965                    42608 
               b'Cortex'          b'Cortical hem'          b'Diencephalon' 
                   44764                     2107                    10338 
      b'Dorsal midbrain'     b'Enthorinal cortex'      b'Forebrain cortex' 
                   13075                     3230                     4769 
            b'Forebrain' b'Frontotemporal cortex'                  b'Head' 
                   58623                     6732                     2034 
            b'Hindbrain'           b'Hippocampus'          b'Hypothalamus' 
                   14771                    10478                    14098 
         b'Lower cortex'               b'Medulla'         b'Mesencephalon' 
                    5446                    38461                    48963 
             b'Midbrain'      b'Occipital cortex'                  b'Pons' 
                    9984                     4063                    18849 
           b'Pons/Cereb'          b'Pons/Medulla'              b'Striatum' 
                   10061                     4109                    28578 
            b'Subcortex' b'Subcortical forebrain'      b'Tel/diencephalon' 
                    8736                    16143                     5033 
        b'Telencephalon'              b'Thalamus'          b'Upper cortex' 
                    2161                    33097                     4043 
     b'Ventral midbrain' 
                    4959 
                 development_stage_ontology_term_id
CellClass         HsapDv:0000099 HsapDv:0000100 HsapDv:0000101 HsapDv:0000102
  b'Erythrocyte'             178            489             42            308
  b'Fibroblast'             4009            117              0            470
  b'Glioblast'                 2           1064           1019           6072
  b'Immune'                   72            329             31            250
  b'Neural crest'            113             48              0             22
  b'Neuroblast'             5254          37245           2935          12777
  b'Neuron'                 8655          64546           5811          37036
  b'Neuronal IPC'            655          10264           2101           5755
  b'Oligo'                     1             17              8            175
  b'Placodes'                104            155              0              1
  b'Radial glia'           17349          95493           4828          20056
  b'Vascular'                 98            564             68            336
                 development_stage_ontology_term_id
CellClass         HsapDv:0000103 HsapDv:0000104 HsapDv:0000105 HsapDv:0000106
  b'Erythrocyte'             126             27            460            568
  b'Fibroblast'              475              0            439             15
  b'Glioblast'              5359           3168           5959           7458
  b'Immune'                  139             49            326            284
  b'Neural crest'             32              0             34             12
  b'Neuroblast'             4949           4828           6165           5858
  b'Neuron'                13509          10008          12009           6640
  b'Neuronal IPC'           2934           2180           2627           2449
  b'Oligo'                   208             58            235            456
  b'Placodes'                  2              0              0              0
  b'Radial glia'            6894           6247           2118           1697
  b'Vascular'                193             57            499            520
                 development_stage_ontology_term_id
CellClass         HsapDv:0000107 HsapDv:0000108
  b'Erythrocyte'              58            427
  b'Fibroblast'               37            201
  b'Glioblast'              3073           6456
  b'Immune'                  164            665
  b'Neural crest'              6              1
  b'Neuroblast'             1929           4135
  b'Neuron'                 3763           6521
  b'Neuronal IPC'           1223           2625
  b'Oligo'                   311            402
  b'Placodes'                  0              0
  b'Radial glia'             839            851
  b'Vascular'                227            894
Loading required package: AnnotationDbi

Attaching package: 'AnnotationDbi'
The following object is masked from 'package:dplyr':

    select
'select()' returned 1:many mapping between keys and columns
            used   (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells  11871296  634.0   31776362  1697.1  189401815 10115.2
Vcells 196828999 1501.7 5437575778 41485.5 6257076648 47737.8
Normalizing layer: counts
Centering and scaling data matrix
Finding variable features for layer counts
PC_ 1 
Positive:  IFITM3, FN1, IGFBP7, B2M, COL4A1, GNG11, FCGRT, HLA.E, S100A11, EGFL7 
       ENG, COBLL1, FLT1, KLF2, ADGRF5, ESAM, A2M, MYL12A, C1orf54, DLC1 
       ETS1, CYBA, COL4A2, SERPINH1, FOXQ1, MYH9, FLI1, ITM2A, NID1, VAMP5 
Negative:  CADM1, STMN2, DCC, DLGAP1, HES6, GAP43, FABP7, MYT1L, PPP2R2B, BCL11B 
       NRG1, EPHA5, SLC44A5, LINC01122, ADGRV1, GRIA1, LOC101927314, GRIA2, ASCL1, DPP10 
       TENM2, SOX6, KCNB2, PCDH9, GRIK2, INA, NKAIN3, ERBB4, GRIA4, NCAM2 
PC_ 2 
Positive:  C1QC, TYROBP, AIF1, C1QB, SPP1, C1QA, LAPTM5, CSF1R, DOCK8, CD53 
       SAMSN1, LY86, CD68, FOLR2, TREM2, PLD4, CX3CR1, ITGB2, RGS10, FCER1G 
       C3, APBB1IP, PTPRC, VSIG4, ADAM28, HPGDS, CYBB, SPI1, FCGR1A, MNDA 
Negative:  CALD1, SPARC, FN1, COL4A2, COL4A1, CDH11, LAMA4, AKAP12, PTN, LHFPL6 
       DLC1, RORA, IFITM3, COL5A2, GNG11, COBLL1, VIM, BGN, IGFBP7, SEMA5A 
       FOXC1, COL1A2, SPTBN1, UACA, MYO1B, COL18A1, NID1, ITIH5, EDNRA, NR2F2 
PC_ 3 
Positive:  COL1A2, PLAC9, COL1A1, PCOLCE, COLEC12, LUM, LGALS1, LAMA2, FRZB, COL6A3 
       EDNRA, ATP1A2, ADAM12, SIDT1, TMEM132C, SLC6A13, COL18A1, TBX18, COL6A1, OGN 
       ABCA9, ISLR, CYP1B1, ITIH5, CD248, BMP5, MYOF, SLC6A1, PTGDS, ITGA8 
Negative:  CLDN5, ADGRL4, SOX18, SLC7A5, SLC38A5, CD34, PTPRB, TIE1, EDN1, ROBO4 
       SLC7A1, CD93, TM4SF18, DIPK2B, ICAM2, SRARP, KDR, ENSG00000279686, SOX17, FLT1 
       PODXL, VWF, AFAP1L1, SPINK8, TEK, ST8SIA6, SLC2A1, PRKCH, ABCB1, MMP28 
PC_ 4 
Positive:  CCDC3, RGS5, KCNJ8, HIGD1B, ABCC9, ITGA1, FOXS1, TBX2, RASL12, MIR4435.2HG 
       ENSG00000280878, CYTOR, HEYL, FAM162B, CSPG4, PRELP, SLC38A11, NODAL, GUCY1A2, AGRN 
       PDGFRB, LINC01099, ENSG00000249669, ADAMTS18, TESC, TFPI, FOXF2, GJC1, PRKG1, MGLL 
Negative:  PTGDS, OGN, ISLR, COL6A3, COL1A1, SLC7A11, CYP1B1, SLC6A13, CXCL12, KCNK2 
       COL15A1, BMP5, EDN3, BMP6, ARHGAP20, CMBL, FAP, SERPIND1, COL13A1, VCAN 
       TMEM132C, ITGA8, TGFBR3, WNT4, WFIKKN2, LAMA2, AHNAK, SNED1, C16orf89, CTHRC1 
PC_ 5 
Positive:  KCND2, PCDH15, CSMD1, OPCML, OLIG1, DPP6, NTM, LRRC4C, CNTN1, LSAMP 
       LHFPL3, CA10, SCRG1, CSMD3, OLIG2, GRID2, MDGA2, S100B, NXPH1, LRRTM4 
       SCN1A, NCAM2, BRINP3, PMP2, PPP2R2B, RIT2, GRID1, NKX2.2, GRIA2, SGCZ 
Negative:  AHSP, HBA1, ALAS2, HBA2, HBM, SLC25A37, ENSG00000284931, HBG1, GYPB, GYPA 
       HEMGN, HBQ1, SLC4A1, GYPC, SELENBP1, HBB, MYL4, ENSG00000239920, EPB42, SMIM1 
       SNCA, MT1G, HBE1, FECH, KLF1, BPGM, C17orf99, RHAG, MT1E, SPTA1 
01:08:26 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by 'spam'
01:08:26 Read 17262 rows and found 30 numeric columns
01:08:26 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by 'spam'
01:08:26 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:08:28 Writing NN index file to temp file /tmp/Rtmp0nn1gr/file1cf06d9808b
01:08:28 Searching Annoy index using 1 thread, search_k = 3000
01:08:32 Annoy recall = 99.79%
01:08:33 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
01:08:33 69 smooth knn distance failures
01:08:35 Initializing from normalized Laplacian + noise (using RSpectra)
01:08:37 Commencing optimization for 200 epochs, with 703070 positive edges
01:08:45 Optimization finished
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 17262
Number of edges: 592468

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9375
Number of communities: 31
Elapsed time: 1 seconds
            used   (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells  11880698  634.5   31776362  1697.1  189401815 10115.2
Vcells 854199639 6517.1 2784038800 21240.6 6257076648 47737.8

Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = FALSE.

Saving 30 x 15 in image

Saving 15 x 8 in image
Saving 8 x 6 in image

Saving 12 x 6 in image

Saving 10 x 40 in image
            used  (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells  12155638 649.2   31776362  1697.1  189401815 10115.2
Vcells 123997398 946.1 2227231040 16992.5 6257076648 47737.8

R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] org.Hs.eg.db_3.17.0         AnnotationDbi_1.64.1       
 [3] scSorter_0.0.2              clustree_0.5.1             
 [5] ggraph_2.1.0                CATALYST_1.24.0            
 [7] reshape2_1.4.4              pals_1.8                   
 [9] gprofiler2_0.2.2            viridis_0.6.4              
[11] viridisLite_0.4.2           cowplot_1.1.1              
[13] randomcoloR_1.1.0.1         RCurl_1.98-1.13            
[15] RColorBrewer_1.1-3          data.table_1.14.10         
[17] lubridate_1.9.3             forcats_1.0.0              
[19] stringr_1.5.1               dplyr_1.1.4                
[21] purrr_1.0.2                 readr_2.1.4                
[23] tidyr_1.3.0                 tibble_3.2.1               
[25] tidyverse_2.0.0             scater_1.28.0              
[27] scuttle_1.10.3              Seurat_5.0.1               
[29] SeuratObject_5.0.1          sp_2.1-2                   
[31] SingleCellExperiment_1.24.0 ggpubr_0.6.0               
[33] ggplot2_3.4.4               SingleR_2.2.0              
[35] SummarizedExperiment_1.32.0 Biobase_2.62.0             
[37] GenomicRanges_1.54.1        GenomeInfoDb_1.38.1        
[39] IRanges_2.36.0              S4Vectors_0.40.2           
[41] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[43] matrixStats_1.1.0           workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] dichromat_2.0-0.1           goftest_1.2-3              
  [3] DT_0.31                     Biostrings_2.68.1          
  [5] TH.data_1.1-2               vctrs_0.6.5                
  [7] spatstat.random_3.2-2       digest_0.6.33              
  [9] png_0.1-8                   shape_1.4.6                
 [11] git2r_0.33.0                ggrepel_0.9.4              
 [13] deldir_2.0-2                parallelly_1.36.0          
 [15] MASS_7.3-60                 httpuv_1.6.13              
 [17] foreach_1.5.2               withr_2.5.2                
 [19] ggrastr_1.0.2               xfun_0.41                  
 [21] ellipsis_0.3.2              survival_3.5-7             
 [23] memoise_2.0.1               ggbeeswarm_0.7.2           
 [25] RProtoBufLib_2.12.1         drc_3.0-1                  
 [27] systemfonts_1.0.5           ragg_1.2.7                 
 [29] zoo_1.8-12                  GlobalOptions_0.1.2        
 [31] gtools_3.9.5                V8_4.4.1                   
 [33] pbapply_1.7-2               KEGGREST_1.40.1            
 [35] promises_1.2.1              httr_1.4.7                 
 [37] rstatix_0.7.2               globals_0.16.2             
 [39] fitdistrplus_1.1-11         ps_1.7.5                   
 [41] rstudioapi_0.15.0           miniUI_0.1.1.1             
 [43] generics_0.1.3              processx_3.8.3             
 [45] curl_5.2.0                  zlibbioc_1.48.0            
 [47] ScaledMatrix_1.8.1          polyclip_1.10-6            
 [49] GenomeInfoDbData_1.2.11     SparseArray_1.2.2          
 [51] xtable_1.8-4                doParallel_1.0.17          
 [53] evaluate_0.23               S4Arrays_1.2.0             
 [55] hms_1.1.3                   irlba_2.3.5.1              
 [57] colorspace_2.1-0            ROCR_1.0-11                
 [59] reticulate_1.34.0           spatstat.data_3.0-3        
 [61] magrittr_2.0.3              lmtest_0.9-40              
 [63] later_1.3.2                 lattice_0.22-5             
 [65] mapproj_1.2.11              spatstat.geom_3.2-7        
 [67] future.apply_1.11.0         getPass_0.2-4              
 [69] scattermore_1.2             XML_3.99-0.16              
 [71] RcppAnnoy_0.0.21            pillar_1.9.0               
 [73] nlme_3.1-164                iterators_1.0.14           
 [75] compiler_4.3.1              beachmat_2.16.0            
 [77] RSpectra_0.16-1             stringi_1.8.3              
 [79] tensor_1.5                  plyr_1.8.9                 
 [81] crayon_1.5.2                abind_1.4-5                
 [83] bit_4.0.5                   graphlayouts_1.0.2         
 [85] sandwich_3.1-0              whisker_0.4.1              
 [87] codetools_0.2-19            multcomp_1.4-25            
 [89] textshaping_0.3.7           BiocSingular_1.16.0        
 [91] crosstalk_1.2.1             bslib_0.6.1                
 [93] GetoptLong_1.0.5            plotly_4.10.3              
 [95] mime_0.12                   splines_4.3.1              
 [97] circlize_0.4.15             Rcpp_1.0.11                
 [99] fastDummies_1.7.3           sparseMatrixStats_1.12.2   
[101] blob_1.2.4                  knitr_1.45                 
[103] utf8_1.2.4                  clue_0.3-65                
[105] fs_1.6.3                    listenv_0.9.0              
[107] checkmate_2.3.1             nnls_1.5                   
[109] DelayedMatrixStats_1.22.6   ggsignif_0.6.4             
[111] Matrix_1.6-4                callr_3.7.3                
[113] tzdb_0.4.0                  svglite_2.1.3              
[115] tweenr_2.0.2                pkgconfig_2.0.3            
[117] pheatmap_1.0.12             tools_4.3.1                
[119] cachem_1.0.8                RSQLite_2.3.4              
[121] DBI_1.1.3                   fastmap_1.1.1              
[123] rmarkdown_2.25              scales_1.3.0               
[125] grid_4.3.1                  ica_1.0-3                  
[127] broom_1.0.5                 sass_0.4.8                 
[129] patchwork_1.1.3             dotCall64_1.1-1            
[131] carData_3.0-5               RANN_2.6.1                 
[133] farver_2.1.1                tidygraph_1.2.3            
[135] yaml_2.3.8                  cli_3.6.2                  
[137] leiden_0.4.3.1              lifecycle_1.0.4            
[139] uwot_0.1.16                 mvtnorm_1.2-4              
[141] backports_1.4.1             BiocParallel_1.34.2        
[143] cytolib_2.12.1              timechange_0.2.0           
[145] gtable_0.3.4                rjson_0.2.21               
[147] ggridges_0.5.4              progressr_0.14.0           
[149] parallel_4.3.1              limma_3.56.2               
[151] jsonlite_1.8.8              RcppHNSW_0.5.0             
[153] bitops_1.0-7                bit64_4.0.5                
[155] openxlsx2_1.2               Rtsne_0.17                 
[157] FlowSOM_2.8.0               spatstat.utils_3.0-4       
[159] BiocNeighbors_1.18.0        zip_2.3.0                  
[161] flowCore_2.12.2             jquerylib_0.1.4            
[163] highr_0.10                  lazyeval_0.2.2             
[165] shiny_1.8.0                 ConsensusClusterPlus_1.64.0
[167] htmltools_0.5.7             sctransform_0.4.1          
[169] glue_1.6.2                  spam_2.10-0                
[171] XVector_0.42.0              rprojroot_2.0.4            
[173] gridExtra_2.3               igraph_1.6.0               
[175] R6_2.5.1                    labeling_0.4.3             
[177] cluster_2.1.6               DelayedArray_0.28.0        
[179] tidyselect_1.2.0            vipor_0.4.5                
[181] plotrix_3.8-4               maps_3.4.1.1               
[183] ggforce_0.4.1               car_3.1-2                  
[185] future_1.33.0               rsvd_1.0.5                 
[187] munsell_0.5.0               KernSmooth_2.23-22         
[189] htmlwidgets_1.6.4           ComplexHeatmap_2.16.0      
[191] rlang_1.1.2                 spatstat.sparse_3.0-3      
[193] spatstat.explore_3.2-5      colorRamps_2.3.1           
[195] ggnewscale_0.4.9            fansi_1.0.6                
[197] beeswarm_0.4.0